KR20140112554A - System for Fluid Processing Networks - Google Patents

Abstract

CLAIMS What is claimed is: 1. A method for monitoring a fluid treatment network comprising a plurality of fluid treatment zones, the method comprising: receiving a current parameter value measured at a known area of the network; Determining an area of the active network from the measured current parameter value and deeming the other area as inactive; Subtracting the inactive region of the network from the model of the fluid treatment network to provide a current active network model; Determining a current parameter value of the current active network in an area at least remote from the known area and the parameter value in the distant area being determined from the measured current parameter value and the current active network model; Determining, based on the current parameter value, whether one or more preset boundaries are violated; And performing a predetermined action if one or more of the boundaries is violated.

Description

[0001] SYSTEM FOR FLUID PROCESSING NETWORKS [0002]

The present invention relates to a system for monitoring and / or controlling a fluid treatment network.

A network for fluid handling, such as a petroleum production facility, refinery, or chemical plant, may comprise a number of components, including, but not limited to, multiple valves, pipe segments, and fluid chambers, . Such a treatment network may be provided with a safety function, for example a fluid discharge network, such as flare networks, to maintain pressure within the network or within a part of the fluid treatment network below the safety limit.

The fluid delivery network may be a sub-network of the fluid treatment network and may include, but is not limited to, for removal of such fluids from a treatment network to a safe location, including discharge into the atmosphere or flaring Together with fluid flowing through the components of the network, is comprised of a number of components, including, but not limited to, a plurality of pipe segments, valves, and fluid chambers.

The fluid treatment network may be provided with sensors including, but not limited to, fluid pressure sensors, fluid temperature sensors, and metal wall temperature sensors. However, these sensors only measure the characteristic values in the specific area in which the sensors are located, leaving an area of unknown pressure and / or temperature between the measurement areas.

The present invention provides a system for monitoring and / or controlling a fluid treatment network.

In a first aspect, the present invention provides a method of monitoring a fluid treatment network comprising a plurality of fluid treatment zones, the method comprising the steps of:

Receiving a current parameter value measured in a known area of the network;

Determining an area of the active network from the measured current parameter value and deeming the other area as inactive;

Subtracting the inactive region of the network from the model of the fluid treatment network to provide a current active network model;

Determining a current parameter value of the current active network in an area at least remote from the known area and the parameter value in the distant area being determined using the measured current parameter value and the current active network model ;

Determining, based on the current parameter value, whether one or more preset boundaries are violated; And

Performing a predetermined action if one or more boundaries are violated.

Optionally, the current active network model is updated periodically using periodically received update values of the measured current parameter values.

Optionally, the activated non-active area, or deactivated active area, is determined from the updated value of the measured current parameter value.

Optionally, the measured current parameter value, the determined current parameter value, and the preset parameter boundary are selected from fluid pressure, fluid temperature, pipe and / or vessel wall temperature, fluid flow rate, and liquid level in the vessel.

[0520] Optionally the parameter boundary comprises a predetermined unchanged boundary.

Optionally, the parameter boundary may comprise varying boundaries or mathematical constraints derived from one or more parameters.

Optionally, the at least one predetermined risk is combined with a dominant parameter present outside the parameter boundary.

Optionally, predetermined hazards include pipe cracking hazards, pipe clogging hazards and explosion hazards.

Optionally, the predetermined operation is selected from one or more of issuing a notification to a network operator and issuing an instruction to an automated network control system.

Optionally, the notification includes identification of a risk associated with the current parameter value that violates one or more of the boundaries.

Optionally, the current parameter value is determined for a known area of the current active network to which the measured parameter value is to be received.

Optionally, the current parameter values measured for a known area are replaced with corresponding determined current parameter values for the same area in a manner consistent with the model of the fluid treatment network.

Optionally, the current active network model comprises a setting value of the network based on a received setting value of the network.

Optionally, the setting value of the network includes a valve setting value of the network.

Optionally, one or more of the predetermined boundaries apply to one or more areas of the network, respectively.

Optionally, the network is a flare network.

In a second aspect, the present invention provides computer program code for causing a computer to perform the method according to the first aspect when executed on a computer.

In a third aspect, the present invention provides a transmission medium having computer readable code that, when executed on a computer, causes the computer to perform the method according to the first aspect.

In a fourth aspect, the present invention provides a computer program product comprising computer readable code according to the third aspect.

In a fifth aspect, the present invention provides a machine readable storage medium comprising: And executable program instructions embodied in the machine-readable storage medium that when executed by the programmable system cause the system to perform the method according to the first aspect.

In a sixth aspect, the present invention provides a method of monitoring a fluid treatment network formed of a plurality of fluid treatment components, each fluid treatment component being associated with one or more predetermined component models, the method comprising: .

Receive a current parameter value measured in a known area of the network;

Generating a model of the fluid treatment network from selection of the predetermined part model;

Determining a current parameter value of the network in an area at least remote from the known area, wherein the parameter value in the remote area is determined using the measured current parameter value and the network model;

Determine if the current parameter value violates one or more predetermined boundaries; And

Performing a predetermined operation if one or more of the boundaries is violated,

Wherein one or more of the fluid treatment components of the network are each associated with two or more of the predetermined component models,

For each part coupled with two or more predetermined part models, one of the two or more predetermined part models is used for generating a model of the fluid treatment network according to at least one condition of the fluid treatment network Selected for.

Wherein the conditions of the fluid treatment network for determining a part model for a particular part used in creating a model of the fluid treatment network according to the sixth aspect are selected from one or more current parameter values of the network; One or more expected future parameter values of the network; And the presence of one or more chemical species of the fluid treatment network.

Optionally, one or more parts of the network different from the sixth aspect are combined into at least one part model used when generating a model for all conditions of the fluid treatment network.

Optionally, the model according to the sixth aspect is modified in response to a change in state of the fluid treatment network causing a change in a selection of a part model to be used when generating the model.

The measured current parameter value, the determined current parameter value and the predetermined parameter boundary according to the sixth aspect are selected from fluid pressure, fluid temperature, pipe and / or vessel wall temperature, fluid flow rate, and liquid level in the vessel Is selected.

Optionally the parameter boundary according to the sixth aspect comprises a predetermined unchanged boundary.

Optionally, the parameter boundary according to the sixth aspect comprises a mathematical constraint derived from a changing boundary or one or more parameter values.

Optionally, one or more of said predetermined boundaries according to the sixth aspect apply to each of one or more predetermined areas of the network.

Optionally, at least one predetermined risk according to the sixth aspect is combined with a given parameter that violates the parameter boundary.

Optionally, the predetermined risk according to the sixth aspect includes the risk of pipe breakage, the risk of pipe clogging and the risk of explosion.

Optionally the predetermined operation according to the sixth aspect is selected from one or more of issuing a notification to a network operator and issuing an instruction to an automated network control system.

Optionally, the notification according to the sixth aspect includes identification of a risk associated with the current parameter value that violates one or more of the boundaries.

Optionally, a current parameter value according to the sixth aspect is determined for a known area of the network to which the measured parameter value is to be received.

Alternatively, the measured current parameter values for known regions according to the sixth aspect are replaced with corresponding determined current parameter values for the same region in a manner consistent with the model of the fluid treatment network.

Optionally the network model according to the sixth aspect comprises a setting value of the network based on a received setting value of the network.

Optionally, a setting value of the network according to the sixth aspect includes a valve setting value of the network.

Optionally the plurality of fluid treatment components according to the sixth aspect include a pipe segment, a valve and a fluid chamber.

Optionally, the network according to the sixth aspect is a flare network.

In a seventh aspect, the present invention provides computer program code for causing a computer to perform the method according to the sixth aspect when executed on a computer.

In an eighth aspect, the present invention provides a transmission medium having computer readable code that, when executed on a computer, causes the computer to perform the method according to the seventh aspect.

In a ninth aspect, the present invention provides a computer program product comprising computer readable code according to the eighth aspect.

In a tenth aspect, the present invention provides a computer readable storage medium, And executable program instructions embodied in the machine-readable storage medium that when executed by the programmable system cause the system to perform the method according to the sixth aspect.

In an eleventh aspect, the present invention provides a method of monitoring a fluid treatment network, the method comprising the steps of:

Receiving a current parameter value measured in a known area of the network;

Determining a current parameter value in an area at least distant from a known area and the parameter value in the distant area being determined by the application of the measured parameter value to a model of the network;

Selecting one or more risks to be analyzed from a predetermined risk group and each predetermined risk being associated with one or more areas of the network;

Determining for each selected risk that the risk in one or more of the combined regions of the network exceeds a predetermined acceptable risk limit for the risk in one or more areas; And

And performing a predetermined action if the risk exceeds a predetermined acceptable risk limit,

The risk is selected for analysis if the current parameter in one or more of the areas associated with the risk meets a predetermined risk selection requirement.

Optionally according to the eleventh aspect, one or more of said predetermined risk groups are not selected for analysis.

Optionally, according to the eleventh aspect, each predetermined risk defines at least one parameter boundary that should not be violated.

Optionally, according to the eleventh aspect, the measured current parameter value, the boundary and the determined current parameter value and the predetermined parameter boundary are determined based on fluid pressure, fluid temperature, pipe and / or vessel wall temperature, Is selected from the liquid level in the vessel.

Optionally according to the eleventh aspect, the parameter boundary comprises a varying boundary or mathematical constraint derived from one or more parameter values.

Optionally according to the eleventh aspect, the predetermined operation is selected from one or more of issuing a notification to a network operator and issuing an instruction to an automated network control system.

Optionally, according to the eleventh aspect, said notification comprises an identification of an exceeded risk limit.

Optionally according to the eleventh aspect, predetermined hazards include pipe cracking hazards, pipe clogging hazards and explosion hazards.

Optionally according to the eleventh aspect, the current parameter value is determined for a known area of the current active network for which the measured parameter value is to be received.

Optionally according to the eleventh aspect, measured current parameter values for known regions are replaced with corresponding determined current parameter values for the same region in a manner consistent with the model of the fluid treatment network.

Optionally according to the eleventh aspect, the current network model comprises a setting value of the network based on a received setting value of the network.

Optionally, according to the eleventh aspect, the setting value of the network includes a valve setting value of the network.

Optionally according to the eleventh aspect, the network is a flare network.

In a twelfth aspect, the present invention provides computer program code for causing a computer to perform the method according to the eleventh aspect when executed on a computer.

In a thirteenth aspect, the present invention provides a transmission medium having computer readable code for causing the computer to perform the method according to the eleventh aspect when executed on a computer.

In a fourteenth aspect, the present invention provides a computer program product comprising computer readable code according to the thirteenth aspect.

In a fifteenth aspect, the present invention provides a machine readable storage medium, And executable program instructions embodied in the machine-readable storage medium that when executed by the programmable system cause the system to perform the method according to the eleventh aspect.

In a sixteenth aspect, the present invention provides a method of monitoring a fluid treatment network, the method comprising the steps of:

Receiving a current parameter value measured in a known area of the network;

Receiving a setting value of the network;

Determining a current parameter value in an area at least distant from the known area and the parameter value in the distant area being determined by the application of the measured parameter value to a model of the network;

Predicting a parameter value at a future time when a predetermined change changes the setting of the network or when a change does not change the setting of the network; And

Performing a predetermined operation if a predetermined change or no change is determined to have the effect of causing one or more of the predicted parameter values to violate a predetermined parameter boundary.

Optionally, the setting value of the network according to the sixteenth aspect includes a valve setting value of the network.

The measured current parameter value, the determined current parameter value, and the predetermined parameter boundary, according to the sixteenth aspect, are selected from the group consisting of fluid pressure, fluid temperature, pipe and / or vessel wall temperature, fluid flow rate, Level.

Optionally according to the sixteenth aspect, the parameter boundary comprises a predetermined unchanged boundary.

Optionally according to the sixteenth aspect, the parameter boundary comprises a varying boundary or mathematical constraint derived from one or more parameters.

Optionally according to the sixteenth aspect, the at least one predetermined risk is combined with a dominant parameter present outside the parameter boundary.

Optionally, according to the sixteenth aspect, predetermined hazards include pipe cracking hazards, pipe clogging hazards and explosion hazards.

Optionally according to the sixteenth aspect, the predetermined operation is selected from one or more of issuing a notification to a network operator and issuing an instruction to an automated network control system.

Optionally according to the sixteenth aspect, the notification includes identifying the risk associated with the current parameter value that violates one or more of the boundaries.

Optionally, according to the sixteenth aspect, the current parameter value is determined for a known area of the current network for which the measured parameter value is to be received.

Optionally, measured current parameter values for known regions, according to the sixteenth aspect, are replaced with corresponding determined current parameter values for the same region in a manner consistent with the model of the fluid treatment network.

Optionally according to the sixteenth aspect, the current active network model comprises a setting value of the network based on a received setting value of the network.

Optionally, the setting value of the network according to the sixteenth aspect includes a valve setting value of the network.

Optionally, one or more of the predetermined boundaries according to the sixteenth aspect are applied to one or more predetermined areas of the network, respectively.

Optionally according to the sixteenth aspect, the network is a flare network.

In a seventeenth aspect, the present invention provides computer program code for causing a computer to perform the method according to the sixteenth aspect when executed on a computer.

In an eighteenth aspect, the present invention provides a transmission medium having computer readable code for causing the computer to perform the method according to the sixteenth aspect when executed on a computer.

In a nineteenth aspect, the invention provides a computer program product comprising computer readable code according to the eighteenth aspect.

In a twentieth aspect, the present invention provides a machine-readable storage medium; And executable program instructions embodied in the machine-readable storage medium that when executed by the programmable system cause the system to perform the method according to the sixteenth aspect.

In a twenty-first aspect, the present invention provides a method of monitoring a fluid ejection sub-network of a fluid treatment network, the method comprising:

Receiving current measured parameter values in a known region of the sub-network;

Determining a current parameter value of the sub-network in an area at least remote from the known area, the parameter value being determined using the measured current parameter value and the current active network model;

Determine whether one or more preset boundaries are violated based on the current parameter value;

And performs predetermined operations if one or more of the boundaries is violated.

Optionally the fluid ejection sub-network according to the twenty-first aspect is a flare network.

The measured current parameter value, the determined current parameter value and the preset parameter boundary according to the twenty-first aspect are selected from the group consisting of fluid pressure, fluid temperature, pipe and / or vessel wall temperature, fluid flow rate, .

Optionally the parameter boundary according to the twenty-first aspect comprises a predetermined unchanged boundary.

Optionally, the parameter boundary according to the twenty-first aspect includes varying bounds or mathematical constraints derived from including one or more parameters.

Optionally, at least one predetermined risk according to the twenty-first aspect is combined with a dominant parameter present outside the parameter boundary.

Optionally, the predetermined risk according to the twenty-first aspect includes the risk of pipe breakage, the risk of pipe clogging and the risk of explosion.

Optionally the predetermined operation according to the twenty-first aspect is selected from one or more of issuing a notification to a network operator and issuing an instruction to an automated network control system.

Optionally, the notification according to the twenty-first aspect includes identifying the risk associated with the current parameter value that violates one or more of the boundaries.

Optionally, a current parameter value according to the twenty-first aspect is determined for a known area of the sub-network for which the measured parameter value is to be received.

Optionally, the measured current parameter value for a known region according to the twenty-first aspect is replaced with a corresponding determined current parameter value for the same region in a manner consistent with the model of the fluid treatment network.

Optionally, the sub-network model according to the twenty-first aspect includes setting values of the network based on the received setting values of the sub-network.

Optionally, a setting value of the sub-network according to the twenty-first aspect includes a valve setting value of the network.

Optionally, one or more of the predetermined boundaries according to the twenty-first aspect are applied to one or more areas of the network, respectively.

In a twenty-second aspect, the present invention provides computer program code for causing a computer to perform the method according to the twenty-first aspect when executed on a computer.

In a twenty-third aspect, the present invention provides a transmission medium having computer readable code that, when executed on a computer, causes the computer to perform the method according to the twenty-first aspect.

In a twenty-fourth aspect, the invention provides a computer program product comprising computer readable code according to the twenty-third aspect.

In a twenty-fifth aspect, the present invention provides a machine-readable storage medium; And executable program instructions embodied in the machine-readable storage medium to cause the system to perform the method according to the twenty-first aspect, when executed by the programmable system.

In particular, embodiments of the present invention relate to methods and apparatus for on-line monitoring of fluid networks and other systems or factories. In this context, "online" can be used to model real-time or real-time measurements and data, and to model a network, system or plant faster than real-time or real- .

The present invention provides a system for monitoring and / or controlling a fluid treatment network.

The present invention will be described in more detail with reference to the drawings.
Figure 1A illustrates an exemplary fluid treatment network including a flare network.
Figure IB illustrates an exemplary flare network.
Figure 2 illustrates a method for network modeling, such as, for example, a flare network.
Figure 3 illustrates an apparatus for implementing an embodiment of the present invention.
4A illustrates a first method for determining whether a boundary has been violated in accordance with an embodiment of the present invention.
4B illustrates a second method for determining whether a boundary has been violated in accordance with an embodiment of the present invention.
Figure 4c illustrates a third method of determining whether a boundary has been violated in accordance with an embodiment of the present invention.
4D illustrates a fourth method for determining whether a boundary has been violated in accordance with an embodiment of the present invention.
FIG. 5 illustrates a determination method in the case where the future scenario violates the boundary.
Figure 6 illustrates a system in accordance with an embodiment of the present invention.

Referring to FIG. 1A, a fluid treatment network 100 may include a plurality of regions 101. Each region may include at least one fluid inlet for fluid entry into the chamber 105 and a fluid outlet to the outside of the chamber, such as, for example, the outlet valve 107, At least one fluid outlet for the fluid. Each zone may be provided with a path for fluid drainage from the fluidic network after being processed, for example, with the valve 109. Before, during, or after the treatment of the fluid to lower the pressure in the area 101, if the pressure in the area violates a predetermined safety level, each area 101 discharges fluid from the network through the fluid discharge sub- For example, a valve 111, for example. Referring to FIG. 1B, the discharge path of each region includes a pipe leading to one or more flare stacks 115 for igniting the fluid, and a predetermined area of the flare network 110 including one or more headers 113 120 < / RTI >

A sensor (not shown), for example, a fluid pressure sensor and / or a fluid temperature sensor, may be distributed throughout the fluid treatment network, including a fluid ejection sub-network.

Other areas 101 may be provided for other functions and other fluids may be present in the other area 101. [ For example, a petroleum refinery network may include a partial distillation zone that provides a number of different portions of crude oil, and each portion may include other regions or regions of the network for other processing . Exemplary treatments for each portion include, but are not limited to, hydrogenation, alkylation, and catalytic cracking. The petroleum platform may have areas for high pressure, medium pressure and low pressure separation areas of oil and gas.

The fluid in each region may be a liquid state or a gaseous state, and / or a mixture of liquid and gas, and the state may change over time.

In order to ensure that the pressure does not reach a dangerous level in areas where the fluid property values are not directly measured, the fluid treatment network may be modeled such that such characteristic values are determined.

Referring to FIG. 2, as a first step 201, a model of the flare network 110 may be prepared by determining the connection of each component "building block" Exemplary components include, but are not limited to, pipe segments, valves, chambers, and flare. Indications include, but are not limited to, each part having one or more pipe segment dimensions, a chamber such as a pipe segment diameter, a length and a wall thickness; And wall roughness. ≪ RTI ID = 0.0 > [0040] < / RTI >

A model can be a set of mathematical relationships that includes a number of parameters in the network that change over time (e, g, pressure, temperature) and describe the behavior of the network over time. Models are algebraic equations; Ordinary differential equations; Ordinary Minerals and Algebraic Equations; Can be generated from a set of integral and partial derivatives, ordinary differential equations and algebraic equations. Exemplary models may be implemented with commercially available gPROMS software or MATLAB software.

A model parameter value may be a predetermined boundary defined as a safe range for one or more parameters or combination of parameters for operation of the network and / or a boundary value capable of avoiding, for example, failure of all or part of the network The network is considered to be able to operate in a safe and efficient manner if it is maintained within the boundary values for efficient operation of the same network, and / or in excessive operation of the raw materials and energy. The parameter boundaries can be determined at the design of the fluid treatment network based on safety standards, which are recommended taking into account the construction and material construction characteristics.

Exemplary parameters that may be set to boundaries include, but are not limited to, one or more of the following:

(I) fluid pressure boundary;

(Ii) fluid flow boundary;

(Iii) fluid temperature boundary; And

(Iv) metal wall temperature boundaries for components including, but not limited to, pipe segments, valves, and fluid chambers.

The boundaries of the network may include predetermined unchanged boundaries such as, for example, unchanged values (constants) associated with the part properties of the network, and each component of the network may have one or more predetermined unchanged boundaries associated therewith . This predetermined unchanged boundary may be included in the model of the flare network 110 generated in step 201.

The boundaries of the network may include varying (variable) boundaries. The varying boundaries may change over time depending on the functionality of the network and / or the material present within the network at any given time. This varying boundary may not be included in the model generated in step 201, but may be generated in real time depending on the state of the application and the network at a given time.

The boundary can be set, for example, as follows.

(a) a numerical lower limit and / or an upper limit for a particular parameter value; For example, the temperature T _F of the fluid inside the pipe segment needs to always be maintained above a certain temperature limit T _L to avoid the risk of brittle fracture of the pipe; In this case, T _F is a time-varying model parameter for a given pipe constituent material, T _L is a model parameter whose value is a known constant (eg, about -46 ° C for a particular type of carbon steel suitable for low temperature applications);

(b) a relationship between two or more parameter values; For example, to avoid the risk of clogging caused by solid hydrate or solid ice formation in the pipe segment that carries fluid containing water, the fluid temperature T _F inside it is the solid hydrate and ice forming temperature, T _H and T _I, respectively. Thus, in this case, it can be defined in terms of mathematical inequality constraints having the form T _F > T _H and T _F > T _I. These three temperatures, T _F , T _H and T _I, are parameters of the value of the change over time as a function of the fluid composition and pressure as determined by the model solution.

Each boundary can be associated with one or more risks. For example, in the case of a safety boundary, the risk of explosion and rupture may be related to the fluid pressure in a network that violates the boundary fluid pressure value, and the risk of breakage may be related to the pipe temperature in a network that is lower than the minimum pipe temperature .

The risk can be determined to exist as soon as the boundary is violated, or if the boundary remains violated for a predetermined period of time.

If only the flare network 110 is modeled, the representation of the entire network may be partitioned at step 203 into multiple regions or regions 120, such as, for example, region 101, as described with reference to FIG. . Each area can have different functions.

A given border may be a general boundary that may be applied to the entirety of the network, such as the entire flare network 110, or may be a general boundary that may be applied to two or more different areas, such as areas 120 of the flare network 110, It may be different for areas. Exemplary general purpose boundary values may include a general maximum pressure and a general maximum Mach number.

For example, each area, such as the area associated with the set fluid treatment operation, can be divided into sub-areas with different boundaries for other sub-areas. For example, the pipe can be made of one or more materials or a plurality of different segments in thickness, resulting in different pressures and / or temperature boundaries for different sub-regions. The boundary can be applied to a single component or sub-area of the network that includes a combination of network components.

Referring to FIG. 3, the current state estimator 306 is configured to receive a network model from a database 304 that stores the generated model as shown in FIG. 2, including the indication and boundary values of the network. The current state estimator 306 is further configured to receive data 302 from the measurements made by the sensors in the network. Data 302 may be provided to network model 304 to determine real-time values of network parameters, such as current temperature and pressure in the network, in areas remote from the measurement area, as described in more detail with reference to Figures 4A- Is applied. The current state estimator 306 can also analyze the data 302 from the measurement location and correct any discrepancies in this data, for example due to incorrect readings. As a first step, the current state estimator 306 can determine if the data received from the measurement domain is incorrect based on measurements from the network model and other measurement domains, and for the region to be used as the domain of the measured values A more accurate value can be determined. If the measured parameter values do not match each other (in accordance with the physical laws embedded in the model), they can be replaced by the corresponding determined current parameter values such that the parameter values over the network are mutually consistent. Accordingly, the current state estimator 306 can determine the correct value of the parameter over the network, including the measurement area.

It will be appreciated that the method steps described herein as an embodiment may be implemented as an equivalent of circuitry specialized for processing routine or data of computer program code executing on a computer processor. As described below with reference to FIG. 6, in practice such a system may be implemented in a computer architecture capable of executing a number of methods in parallel.

4A illustrates a method for a data processing system configured to determine the state of the flare network 110 during operation. In use, embodiments of the present invention provide for real-time monitoring of the fluid network.

The system is configured to receive the current network data 302 at step 401. The data 302 may include one or more of the following: from a measurement made by the sensor in the flare network 110, such as, for example, pressure and temperature from a fluid pressure sensor and / Of data; And fluid flow data, which is a direct measurement of fluid flow data or data over the range in which the inlet valve 111 of the network 110 is open, and the flow rate can be determined using established engineering techniques. The current network data 302 includes not only the data of the measurement parameters but also the area of the network from which the parameters are measured.

The data 302 only allows the determination of whether the boundary value is violated in the area of the network where the data is taken.

In step 403, the system determines the state of the current network, including parameters such as fluid pressure value and / or fluid temperature value, both in the region where the direct measurement is made and in the region (away region) And to combine the received measurement data with the network model stored in the risk database 304. This is accomplished through a solution of the state estimation problem that applies to a basic set of mathematical equations in the model, including, but not limited to, extended Kalman filter, particle filtering, and others, and based on appropriate algorithms. The state estimation problem and its solution are described, for example, in S. Simon, Optimal Condition Assessment : Kalman, Hindi and Nonlinear Approaches , 2006. (Simon, Optimal State Estimation: Kalman, Hinf and Nonlinear Approaches, Wiley Interscience, 2006).

The current parameter values of the network may be verified at step 405 for the boundary, stored in database 304 or other database, to determine if the boundary is violated. The stored boundaries may be stored in a separate risk database (not shown). The risk database may include a combination of model parameters of the model parameters or a combination of model parameters associated with the risk and whether the warning message should be issued to the driver and whether such messages (e.g., "information", "warning" Etc.) of the values of these parameters which determine the severity of the parameter. If the boundary is violated, an alert may be issued to the system user at step 407. The alert may include one or more of the following: Alerts such as broken boundaries; A message indicating a risk associated with a boundary violation; The severity of the risk; Recommendations for corrective action.

If the network is automated, or if a particular safety system of the network is automated, the system may be configured to send an instruction to the automated network control system to take appropriate corrective action. For example, the valve can be automatically opened to eliminate the increased risk of pressure.

The current network data 302 is periodically updated and the parameter values of the current network status determined in step 403 are updated periodically based on the updated current network so that the current network status determined by the system represents the real- Can be updated.

Referring again to FIG. 1B, one or more regions 120 of the flare network 110 may be inactive during operation of the network. For example, the fluid may flow into or through one or more regions 120, and the fluid may not flow in one or more of the other regions 120.

The data processing system may be configured to analyze only active areas of the network and exclude inactive areas from the model. As used herein, the term "active region" refers to a region in which a fluid flows into or through a region over a minimum of a predetermined flow rate, or within a predetermined time period, or other parameters (e.g., Quot; means an area still changing over time as a result of the relative recent flow of material through the area. As used herein, the term "inactive region" refers to a region in which a fluid does not flow into a region or through a region beyond a minimum of a predetermined flow rate, and does not flow at least over a predetermined period, do. The minimum value of the flow rate may, but need not, be a flow rate of zero (0).

4B, the system tracks and models all possible flow paths from recently opened valves or recently opened and closed valves in, for example, a flare stack, so that current network data 302 is stored in database 304 4A, except that it can be analyzed in step 409 to determine the current active area of the entire network model stored in the network. Areas deemed not to be active may be excluded from the model of the entire network to provide a model of the current active network and the current network state determined in step 403 may be performed only for the current active network.

The active or inactive state in any given region may change over time and the data processing system may periodically re-analyze the current network data 302 to determine which region has changed from active to inactive or from inactive to active . In this way, the current active network determined in step 409 and the current network status of the currently active network generated in step 403 may be periodically updated in real time so that they always remain representative of the current state of the network.

Certain embodiments in accordance with the present invention can automatically track and redefine active areas of a network and use them to calculate, for example, using code routines that monitor predetermined valves of the network between open and closed states can do. Such a code routine may be executed from time to time or in response to a trigger to update and / or redefine the active areas to be included in the analysis.

By creating a model that includes an active area for analysis, higher computation efficiencies can be achieved, and the model can be applied to models that include areas where the value of the parameter representing the fluid flow is at or near zero It can be more powerful.

The total number of risks monitored and potentially identified by the system can be very large. For example, if the pipe is maintained below the first minimum boundary pipe temperature for at least time T1 (e.g., due to the Joule-Thomson effect), then the pipe may be in a fragile condition and thus a first risk associated with the pipe temperature It can be a risk of breakage. If the measured or determined pipe temperature falls below a predetermined minimum pipe temperature boundary, or if the pipe temperature remains below a predetermined minimum pipe temperature for a predetermined maximum time or longer, the predetermined acceptable pipe cracking risk limit may be exceeded have.

Moreover, if the gas flowing through the pipe comprises water vapor, it may be a second hazard of ice and / or other solids such as hydrates formed in the pipe at a temperature below the minimum boundary value, which ultimately leads to clogging of the pipe . When the water vapor flows through the pipe and the measured or determined pipe temperature falls below a predetermined minimum pipe temperature boundary or when the pipe temperature is below a predetermined minimum pipe temperature boundary for at least a predetermined maximum time during which water vapor is flowing through the pipe The predetermined acceptable ice blockage risk limit may be exceeded.

Referring to FIG. 4C, the database 304 including the network model includes all predetermined risks and acceptable risk limits. In another embodiment, the separate risk database may include all predetermined risks and acceptable risk limits. Each item in such a database may include, but is not limited to, the identity of the model parameters associated with the risk, whether the driver needs to be alerted, and whether such messages (e.g., "information", " Quot ;, "warning ", and the like), and the upper and lower limits of such parameter values. The data processing system may be configured to select all or a subset of the predetermined acceptable risk limits for analysis to determine if one or more acceptable risk limits have been exceeded in steps 411 and 413. [

Each of the risks to be analyzed may be associated with a selection request to be made based on the parameter values from the current network conditions determined in step 403. [

For example, referring back to the exemplary dangers of ice formation in a pipe segment, if the measured or determined temperature of the pipe in the pipe or area thereof is above a predetermined level, and / or the water vapor is present in the pipe If not, the system is configured not to monitor the risk of ice formation in a pipe or pipe in a particular area.

The degree of detail that needs to be included in the model depends on the risks being monitored. For example, a model of a pipe segment that can predict the potential formation of ice may be more complex than one can predict only the temperature and pressure in the pipe. The model database 304 may include multiple models of the same part (such as a pipe segment or a container), each such model having a different degree of detail. For example, in the case of a component representing a pipe segment, the model database may include three distinct models: the simplest model may only contain a description of the relationship between the fluid flow in the pipe and the pressure drop in the pipe; Models with moderate complexity may additionally include a description of the temperature change in the pipe wall along the pipe length, with a description of the heat transfer between the wall and fluid in the pipe and between the wall and the ambient atmosphere; Finally, the most detailed model may additionally include a description of the potential formation of solids in the pipe at very low temperatures. The system can then automatically select a model of the appropriate level of detail for each and every part of the network consistent with the risks selected and analyzed to build the model of the entire network. The selection may be made through an evaluation of a predetermined logical condition that includes the current value of the parameters in the network. As an example of the pipe segment described above, the system may switch from a simplest model to a model with intermediate complexity if the fluid temperature T _F in the pipe falls within a predetermined margin of the temperature T _B of brittle fracture of the pipe constituent material Can be; T _F is a parameter computed by the current state estimator 403, the value of which generally changes over time, while T _B is a known constant (a constant value). Moreover, the system can be configured to switch to the most detailed model if the differences between the temperature T _F of the fluid in the pipe and the potential ice or hydrate forming temperatures T _I and T _H , respectively, are less than a preset margin; Both T _I and T _H are parameters that change over time as computed by current state estimator 403. In addition, the selection of an appropriate model for each part can be changed periodically and, if necessary throughout the operation of the system, such a change will cause a real-time reconstruction of the entire network model.

The system may be configured to monitor a particular risk regardless of the current network conditions determined in step 403. [ For example, the system may be configured to always monitor risks that have serious safety implications, such as explosion hazards, which are contrary to the risk limits associated with the hazards that may only affect the efficiency of the network.

By the choice of the risk that the model is analyzed and reconstructed in the manner described above, the processing time and / or processing power required by the system may be scarcely reduced or reduced in effectiveness of the system.

Fig. 4d illustrates the combination of Fig. 4b and Fig. 4c where the inactive area is determined at step 409, omitted from the network model illustrated in Fig. 4b, and a subset of the monitored risks is selected , And the network model is automatically reconfigured to include the part model of the appropriate detail. The network areas are then determined to be deactivated, resulting in a more compact network model, which then allows more risks to be selected for monitoring using more detailed part models if no determination of inactive areas is made.

The data processing system described with reference to Figures 4A-4D is configured to reactivate when the current state of the network violates one or more preset boundaries. The data processing system may further be configured to determine when a particular virtual event should be taken if the network is likely to violate one or more boundaries at some time in the future.

Figure 5 illustrates how the system can run one future scenario starting from the current state 501 of the network, as determined in step 403 described above with reference to Figures 4A-4D. The definition of the future scenario 502 includes, but is not limited to, a scenario in which a scenario needs to be considered, a time range of interest in the scenario (e.g., a specific time starting from the current time) (0) or more input variation (e.g., flow rate through one or more valves 111 in Figure 1B). For example, future scenarios may include the opening of some valves 111 that are currently closed, and / or the closing of some of the valves that are currently open, and / or a change in flow rate, The effect of other properties (e.g., composition and / or temperature) can be determined. The number of such additional inputs may be zero if future scenarios have no change in network configuration and simply determine how the network state evolves over time from the current conditions. In step 503, the system determines whether future scenarios are to be evaluated at the current time. The definitions of the future scenarios are established at predetermined regular time intervals (eg every 10 minutes) and / or one or more criteria (expressed in terms of logical conditions that include the current values of one or more system parameters) It can be specified that it should be executed every time. For example, future scenarios related to virtual events that potentially cause the network to exceed capacity are still to be considered, but only if the system is currently operating near capacity.

At step 503, the system may also proceed with an evaluation of the future scenario after an explicit request issued by a foreign agent, such as, for example, a human operator or other computer program. These future scenarios can be pre-defined globally or partially or completely by the external agent before execution. This functionality allows the system to act as a decision support tool, e.g., providing prior knowledge of the proposed action set effects for future actions of the network.

Assuming that step 503 determines that future scenarios are to be evaluated, then step 504 determines a given current state of the network defined by future scenarios and the set of active areas that need to be considered for additional network input. Using the model database 304, this step may constitute an appropriate subset of the network model describing these active areas as described in step 409 of FIGS. 4B and 4D. By doing so, and applying appropriate criteria to the predicted state of the network, the system can also determine the appropriate degree of modeling detail to be applied to the modeling of each component in the network, as described in step 411 of Figure 4d.

Using the model generated in step 504, the future scenario estimator considers the time to change the input to the network as specified in the definition of the future scenario, predicts the state of the network at the end of the time interval, RTI ID = 0.0 > 505 < / RTI > to perform a dynamic simulation to advance the time for the interval. In a next step 506, the future scenario evaluator evaluates this predicted state against the risk database 507 if one or more of the boundaries are violated, and issues an appropriate warning message. Each of these messages may include, but is not limited to, the identity of the evaluated future scenario, the predicted future time (eg, relative current time) at which the violation is to occur, and the values of the congestion and parameters involved in the violation.

Finally, at step 508, the future scenario evaluator checks whether the end of the time range of interest for the future scenario being evaluated has been reached. In this case, the execution of the scenario is terminated, or the algorithm is repeated from step 504.

The evaluation system is configured to be considered for evaluating one or more future scenarios using one or more respective future scenario evaluators operating concurrently or sequentially, such consideration taking place at regular time intervals during network operations. The analysis of each future scenario may be performed concurrently or sequentially on the same processor, or the analysis may be split among multiple processes. Optionally, each future scenario has a dedicated processor that runs a dedicated future scenario evaluator.

6 illustrates one embodiment of a system as disclosed herein, which illustrates the monitoring of the current state of the flare network and the evaluation of the effect of the number n of future scenarios on the flare network.

The system 306 includes calculation components 306, 601, 602, 603, and 604 that are implemented as general computer code independent of the particular flare network that is the system is applied. Separate instantiation of future scenario evaluator parts is performed for each future scenario under consideration. Each calculation element can be implemented as a separate computer program. Other components may be running on the same or different computer processors, which communicate with each other using well-established inter-process communication protocols such as Message Transfer Interface (MPI) or Parallel Virtual Machine (PVM) protocols.

The flare network and desired operation of the system upon application are defined globally in terms of configuration files 304, 310 and 610. A separate future scenario definition file 610 is provided for each future scenario under consideration. The complete separation between common computing components and network specific configuration files facilitates the installation and maintenance of the system.

During operation of the system, real-time data is received from sensors in the network. Some of these data are used by the influent flow calculator 602 to determine the influent flow into the network if not already directly measured. The data including the computed influent flow is then passed to a flare network state estimator 306 which is a computer code implementation of the algorithm described in Figure 4d and the issued alert is sent to the operator console 604 in the center And transmitted to the monitor 603.

The flare network state estimator 306 also communicates the current state of the system to the future scenario evaluator 601, each of which is responsible for the execution of another predefined future scenario 610. [ Each future scenario evaluator 601 is a computer code implementation of the algorithm described in FIG. Alerts arising from the execution of the corresponding future scenarios are forwarded to the central monitor 604 which forwards them to the operator console 604 in an appropriate form.

The system described herein may enable the following precise determinations: parameters such as fluid temperature and pressure in the network; Prediction of parameters in the future area with or without change; And the risk of network efficiency and / or safety.

As shown in the above description, at least some implementations of the invention described herein include, for example, programming of a processor of one or more servers. Implementations that may include programming include, but are not limited to: generating a model of the fluid treatment network; Determining parameters of the network by applying measured data to the network model; Determining if the determined parameter exceeds a boundary; Predicting the effect of the parameter when there is a change or no change in the setting of the system; Determining if the predicted parameter exceeds a boundary; Taking the predicted action if the determined or predicted parameter exceeds a boundary for that parameter, such as issuing a warning.

The program aspect of the technology may be thought of as a " product "or" article of manufacture "in the form of executable code and / or combined data that is typically carried or embedded in a type of machine-readable medium. "Storage" type media includes some or all of the memory of its associated modules such as supporting electronic systems, computing devices, processors, etc., or various semiconductor memories, tape drives, disk drives, and the like. All or part of the software may be delivered from time to time via the Internet or various communication networks. Such communication may enable, for example, loading of software from one computer or processor to another computer or processor. Thus, other types of media that may have software components include optical and electromagnetic waves, such as those used over physical interfaces between local devices, via wired and optical communication networks and various air links. Physical elements that carry radio waves, such as wired or wireless links, optical links, etc., can also be considered media with software.

Thus, machine-readable media can take many forms, including, but not limited to, non-volatile storage media, carrier wave media, or physical transmission media. Type non-volatile storage media include optical or magnetic disks, such as, for example, storage devices, such as computers. The type volatile storage medium includes dynamic memory, such as main memory of a computer platform. Type transmission media include coaxial cable; A copper wire and an optical fiber, comprising a wire including a wire including a bus within a computer system. The carrier wave transmission medium may take the form of electrical or electronic signals, such as those generated during radio frequency (RF) and infrared (IR) data communications, or sound waves or light waves. Most of these forms of computer readable media may include conveying one or more sequences of one or more instructions to a processor for execution.

The invention has been described primarily with reference to a fluid delivery network, such as, for example, a flare net in a fluid handling network.

However, it will be appreciated that the present invention may be applied to any fluid handling network to create a fluid handling network model, including but not limited to; Applying the measured data to the network model to determine parameters of the fluid treatment network; Determine whether the parameter exceeds a boundary; Predicting the effect on the parameter when there is a change in the setting of the system or not; Determine if the predicted parameter exceeds a boundary; A predetermined action is taken if the parameter determined or predicted for that parameter, such as issuing an alarm, exceeds the boundary. Such alerts can be used, for example, as messages for a general machine control interface, as safety alerts and / or alerts, as messages for human operators.

For example, the present invention may be applied to a fluid treatment network that includes, but is not limited to, one or more hydrogenation reactions, alkylation and / or catalytic cracking zones dedicated to a particular crude oil fraction, Reaction, alkylation, and / or crude oil purification which may have a catalytic cracking zone.

Although the present invention has been described with reference to particular embodiments, it is to be understood that various changes, modifications, and / or combinations of the features disclosed herein will be apparent to those skilled in the art without departing from the scope of the invention as set forth in the following claims It will be possible.

100; A fluid treatment network 101; Multiple regions
103; Inlet valve 105; chamber
107; A discharge valve 109; valve
113; Header 115; Flare stack

Claims (96)

A method for monitoring a fluid treatment network comprising a plurality of fluid treatment zones, the method comprising:
Receiving a current parameter value measured in a known area of the network;
Determining an area of the active network from the measured current parameter value and deeming the other area as inactive;
Subtracting the inactive region of the network from the model of the fluid treatment network to provide a current active network model;
Determining a current parameter value of the current active network in an area at least remote from the known area and the parameter value in the distant area being determined using the measured current parameter value and the current active network model ;
Determining, based on the current parameter value, whether one or more preset boundaries are violated; And
And performing a predetermined operation if one or more of the boundaries is violated. ≪ Desc / Clms Page number 19 > The method according to claim 1,
Wherein the current active network model is updated periodically using periodically received update values of the measured current parameter values. 3. The method of claim 2,
Wherein the activated or inactivated active area is determined from the updated value of the measured current parameter value. 4. The method according to any one of the preceding claims,
Wherein the measured current parameter value, the determined current parameter value and the preset parameter boundary are selected from fluid pressure, fluid temperature, pipe and / or vessel wall temperature, fluid flow rate, and liquid level in the vessel. A method for monitoring a processing network. 4. The method according to any one of the preceding claims,
Wherein the parameter boundary comprises a predetermined unchanged boundary. ≪ RTI ID = 0.0 > 31. < / RTI > 4. The method according to any one of the preceding claims,
Wherein the parameter boundary comprises a varying boundary or mathematical constraint derived from one or more parameters. 4. The method according to any one of the preceding claims,
Wherein the at least one predetermined risk is coupled to a dominant parameter present outside the parameter boundary. 8. The method of claim 7,
Wherein the predetermined risk includes a risk of pipe cracking, a risk of pipe clogging, and an explosion risk. 4. The method according to any one of the preceding claims,
Wherein the predetermined action is selected from one or more of issuing a notification to a network operator and issuing an instruction to an automated network control system. 10. The method of claim 9,
Wherein the notification includes an identification of a risk associated with the current parameter value that violates one or more of the boundaries. 4. The method according to any one of the preceding claims,
Wherein the current parameter value is determined for a known area of the current active network to which the measured parameter value is to be received. 12. The method of claim 11,
Wherein the current parameter values measured for a known area are replaced with corresponding determined current parameter values for the same area in a manner consistent with the model of the fluid treatment network. 4. The method according to any one of the preceding claims,
Wherein the current active network model comprises a setting value of the network based on a received setting value of the network. 15. The method of claim 14,
Wherein the setting value of the network comprises a valve setting value of the network. 4. The method according to any one of the preceding claims,
Wherein the one or more predetermined boundaries are applied to one or more areas of the network, respectively. 4. The method according to any one of the preceding claims,
RTI ID = 0.0 > 1, < / RTI > wherein the network is a flare network. 21. Computer program code for causing a computer to perform the method according to any one of the preceding claims when executed on a computer. 17. A transmission medium having computer readable code for causing the computer to perform the method according to any one of claims 1 to 16 when executed on a computer. 19. A computer program product comprising computer readable code according to claim 18. A machine-readable storage medium; And executable program instructions embodied in the machine-readable storage medium to cause the system to perform the method according to any one of claims 1 to 16 when executed by a programmable system. A method for monitoring a fluid treatment network formed of a plurality of fluid treatment components, each fluid treatment component coupled to one or more predetermined component models, the method comprising:
Receive a current parameter value measured in a known area of the network;
Generating a model of the fluid treatment network from selection of the predetermined part model;
Determining a current parameter value of the network in an area at least remote from the known area, wherein the parameter value in the remote area is determined using the measured current parameter value and the network model;
Determine if the current parameter value violates one or more predetermined boundaries; And
Performing a predetermined operation if one or more of the boundaries is violated,
Wherein one or more of the fluid treatment components of the network are each associated with two or more of the predetermined component models,
For each part coupled with two or more predetermined part models, one of the two or more predetermined part models is used for generating a model of the fluid treatment network according to at least one condition of the fluid treatment network ≪ / RTI > is selected for < RTI ID = 0.0 > a < / RTI > 22. The method of claim 21,
Wherein at least one condition of the fluid treatment network that determines a part model for a particular part used when creating a model of the fluid treatment network is one or more current parameter values of the network; One or more expected future parameter values of the network; And the presence of one or more chemical species of the fluid treatment network. ≪ Desc / Clms Page number 20 > 23. The method according to any one of claims 19 to 22,
Wherein one or more parts of the network are coupled to at least one part model used when generating a model for all conditions of the fluid treatment network. 24. The method according to any one of claims 19 to 23,
Wherein the model is modified in response to a change in state of the fluid treatment network causing a change in a selection of a part model used when generating the model. 25. The method according to any one of claims 19 to 24,
Wherein the measured current parameter value, the determined current parameter value and the predetermined parameter boundary are selected from fluid pressure, fluid temperature, pipe and / or vessel wall temperature, fluid flow rate, and liquid level in the vessel. How to monitor your network. 26. The method according to any one of claims 19 to 25,
Wherein the parameter boundary comprises a predetermined unchanged boundary. ≪ RTI ID = 0.0 > 31. < / RTI > 27. The method according to any one of claims 19 to 26,
Wherein the parameter boundary comprises a mathematical constraint derived from a boundary or one or more parameter values that change. 28. The method according to any one of claims 19 to 27,
Wherein the one or more predetermined boundaries apply to each of one or more predetermined areas of the network. 29. The method according to any one of claims 19 to 28,
Wherein the at least one predetermined risk is coupled to a given parameter that violates a parameter boundary. 30. The method of claim 29,
Wherein the predetermined risk includes a risk of pipe cracking, a risk of pipe clogging, and an explosion risk. 32. The method according to any one of claims 19 to 30,
Wherein the predetermined action is selected from one or more of issuing a notification to a network operator and issuing an instruction to an automated network control system. 32. The method of claim 31,
Wherein the notification includes an identification of a risk associated with the current parameter value that violates one or more of the boundaries. 33. The method according to any one of claims 19 to 32,
Wherein the current parameter value is determined for a known region of the network for which the measured parameter value is to be received. 34. The method of claim 33,
Wherein the measured current parameter values for known regions are replaced with corresponding determined current parameter values for the same region in a manner consistent with the model of the fluid treatment network. 35. The method according to any one of claims 19 to 34,
Wherein the network model comprises a setting value of the network based on a received setting value of the network. 36. The method of claim 35,
Wherein the setting value of the network comprises a valve setting value of the network. 37. The method according to any one of claims 19 to 36,
Wherein the plurality of fluid treatment components comprise a pipe segment, a valve and a fluid chamber. 37. The method according to any one of claims 19 to 37,
RTI ID = 0.0 > 1, < / RTI > wherein the network is a flare network. 38. A computer program code for causing a computer to perform the method according to any one of claims 19 to 38 when executed on a computer. 39. A transmission medium having computer readable code for causing the computer to perform the method according to any of claims 19 to 39 when executed on a computer. 42. A computer program product comprising computer readable code according to claim 40. A machine-readable storage medium; And executable program instructions embodied in the machine-readable storage medium for causing the system to perform the method according to any one of claims 19 to 41 when executed by a programmable system. A method for monitoring a fluid treatment network, the method comprising:
Receiving a current parameter value measured in a known area of the network;
Determining a current parameter value in an area at least distant from a known area and the parameter value in the distant area being determined by the application of the measured parameter value to a model of the network;
Selecting one or more risks to be analyzed from a predetermined risk group and each predetermined risk being associated with one or more areas of the network;
Determining for each selected risk that the risk in one or more of the combined regions of the network exceeds a predetermined acceptable risk limit for the risk in one or more areas; And
And performing a predetermined action if the risk exceeds a predetermined acceptable risk limit,
Wherein the risk is selected for analysis if the current parameter in one or more of the areas associated with the risk meets a predetermined risk selection requirement. 44. The method of claim 43,
Wherein one or more of the predetermined risk groups are not selected for analysis. 45. The method of claim 43 or 44,
Wherein each of the predetermined risks defines at least one parameter boundary that should not be violated. 46. The method of claim 43,
Wherein the measured current parameter value, the boundary and the determined current parameter value and the predetermined parameter boundary are selected from fluid pressure, fluid temperature, pipe and / or vessel wall temperature, fluid flow rate, and liquid level in the vessel Gt; wherein < / RTI > 46. The method according to any one of claims 43 to 46,
Wherein the parameter boundary comprises a varying boundary or mathematical constraint derived from one or more parameter values. A method as claimed in any one of claims 43 to 47,
Wherein the predetermined action is selected from one or more of issuing a notification to a network operator and issuing an instruction to an automated network control system. 49. The method of claim 48,
Wherein the notification comprises an identification of an exceeded risk limit. A method according to any one of claims 43 to 49,
Wherein the predetermined risk includes a risk of pipe cracking, a risk of pipe clogging, and an explosion risk. A method according to any one of claims 43 to 50,
Wherein the current parameter value is determined for a known area of the current active network to which the measured parameter value is to be received. 52. The method of claim 51,
Wherein the measured current parameter values for known regions are replaced with corresponding determined current parameter values for the same region in a manner consistent with the model of the fluid treatment network. 53. The method according to any one of claims 43 to 52,
Wherein the current network model comprises a setting value of the network based on a received setting value of the network. 54. The method of claim 53,
Wherein the setting value of the network comprises a valve setting value of the network. 55. The method according to any one of claims 43 to 54,
RTI ID = 0.0 > 1, < / RTI > wherein the network is a flare network. 55. A computer program code for causing a computer to perform a method according to any one of claims 43 to 55 when executed on a computer. 55. A transmission medium having a computer readable code for causing the computer to perform the method according to any one of claims 43 to 55 when executed on a computer. 58. A computer program product comprising computer readable code according to claim 57. A machine-readable storage medium; And executable program instructions embodied in the machine-readable storage medium to cause the system to perform the method according to any of claims 43 to 55 when executed by a programmable system. A method for monitoring a fluid treatment network, the method comprising:
Receiving a current parameter value measured in a known area of the network;
Receiving a setting value of the network;
Determining a current parameter value in an area at least distant from the known area and the parameter value in the distant area being determined by the application of the measured parameter value to a model of the network;
Predicting a parameter value at a future time when a predetermined change changes the setting of the network or when a change does not change the setting of the network; And
Performing a predetermined operation when a predetermined change or no change is determined to have the effect of causing one or more of the predicted parameter values to violate a predetermined parameter boundary A method for monitoring a fluid treatment network. 60. The method of claim 59,
Wherein the setting value of the network comprises a valve setting value of the network. 62. The method of claim 60 or 61,
Wherein the measured current parameter value, the determined current parameter value and the predetermined parameter boundary are selected from a fluid pressure, a fluid temperature, a pipe and / or vessel wall temperature, a fluid velocity, and a liquid level in the vessel. A method for monitoring a processing network. 62. The method according to any one of claims 60 to 62,
Wherein the parameter boundary comprises a predetermined unchanged boundary. ≪ RTI ID = 0.0 > 31. < / RTI > 63. The method according to any one of claims 60 to 63,
Wherein the parameter boundary comprises a varying boundary or mathematical constraint derived from one or more parameters. 65. The method of any one of claims 60-64,
Wherein the at least one predetermined risk is coupled to a dominant parameter present outside the parameter boundary. 66. The method of claim 65,
Wherein the predetermined risk includes a risk of pipe cracking, a risk of pipe clogging, and an explosion risk. 66. The method according to any one of claims 60 to 66,
Wherein the predetermined action is selected from one or more of issuing a notification to a network operator and issuing an instruction to an automated network control system. 68. The method of claim 67,
Wherein the notification includes an identification of a risk associated with the current parameter value that violates one or more of the boundaries. 69. The method according to any one of claims 60 to 68,
Wherein the current parameter value is determined for a known area of the current network to which the measured parameter value is to be received. 70. The method of claim 69,
Wherein the measured current parameter values for known regions are replaced with corresponding determined current parameter values for the same region in a manner consistent with the model of the fluid treatment network. A method according to any one of claims 60 to 70,
Wherein the current active network model comprises a setting value of the network based on a received setting value of the network. 72. The method according to any one of claims 60 to 71,
Wherein the setting value of the network comprises a valve setting value of the network. 73. The method according to any one of claims 60 to 72,
Wherein the one or more predetermined boundaries are applied to one or more predetermined areas of the network, respectively. 73. The method according to any one of claims 60 to 73,
RTI ID = 0.0 > 1, < / RTI > wherein the network is a flare network. 74. A computer program code for causing a computer to perform the method according to any one of claims 60 to 74 when executed on a computer. 75. A transmission medium having computer readable code for causing the computer to perform the method according to any one of claims 60 to 75 when executed on a computer. A computer program product comprising computer readable code according to claim 77. A machine-readable storage medium; And executable program instructions embodied in the machine-readable storage medium to cause the system to perform the method according to any one of claims 60 to 75 when executed by a programmable system. A method for monitoring a fluid ejection sub-network of a fluid treatment network, the method comprising:
Receiving current measured parameter values in a known region of the sub-network;
Determining a current parameter value of the sub-network in an area at least remote from the known area, the parameter value being determined using the measured current parameter value and the current active network model;
Determine whether one or more preset boundaries are violated based on the current parameter value;
Wherein the predetermined action is performed when one or more of the boundaries is violated. ≪ Desc / Clms Page number 19 > 80. The method of claim 79,
Wherein the fluid ejection sub-network is a flare network. ≪ Desc / Clms Page number 17 > 80. The method of claim 79 or 80,
Wherein the measured current parameter value, the determined current parameter value and the preset parameter boundary are selected from fluid pressure, fluid temperature, pipe and / or vessel wall temperature, fluid flow rate, and liquid level in the vessel. A method for monitoring a fluid ejection sub-network of a processing network. 83. The method according to any one of claims 79 to 81,
Wherein the parameter boundary is a predetermined unchanged boundary. ≪ RTI ID = 0.0 > 31. < / RTI > A method according to any one of claims 79 to 82,
Wherein the parameter boundary is a varying boundary or mathematical constraint derived from including one or more parameters. ≪ Desc / Clms Page number 17 > 83. The method according to any one of claims 79 to 83,
Wherein at least one predetermined risk is coupled to a dominant parameter present outside of the parameter boundary. ≪ RTI ID = 0.0 > 11. < / RTI > 85. The method of claim 84,
Wherein the predetermined risk includes a risk of pipe cracking, a risk of pipe clogging, and an explosion risk. The method according to any one of claims 79 to 85,
Wherein the predetermined action is selected from one or more of issuing a notification to a network operator and issuing an instruction to an automated network control system. ≪ Desc / Clms Page number 19 > 88. The method of claim 86,
Wherein the notification includes identification of a risk associated with the current parameter value that violates one or more boundaries. ≪ RTI ID = 0.0 >< / RTI > 89. The method according to any one of claims 79 to 88,
Wherein the current parameter value is determined for a known region of the sub-network to which the measured parameter value is to be received. 90. The method of claim 88,
Wherein the measured current parameter values for the known regions are replaced with corresponding determined current parameter values for the same region in a manner consistent with the model of the fluid treatment network. How to. 90. The method according to any one of claims 79 to 89,
Wherein the sub-network model comprises a setting value of the network based on a received setting value of the sub-network. 89. The method of claim 90,
Wherein the setting value of the sub-network comprises a valve setting value of the network. ≪ Desc / Clms Page number 20 > 92. The method according to any one of claims 79 to 91,
Wherein the one or more predetermined boundaries are applied to one or more areas of the network, respectively. 92. Computer program code for causing a computer to perform the method according to any one of claims 79 to 92 when executed on a computer. 92. A transmission medium having computer readable code for causing the computer to perform the method according to any one of claims 79 to 92 when executed on a computer. A computer program product comprising computer readable code according to claim 94. A machine-readable storage medium; And an executable program instruction embedded in the machine-readable storage medium for causing the system to perform the method according to any one of claims 79 to 95 when executed by a programmable system.

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